🧠 Mnemonics Reference

Master complex concepts with these powerful memory aids. Each mnemonic is designed to help you remember key concepts and techniques in machine learning and data science.

🧮

NumPy Fundamentals

Test your knowledge of NumPy arrays, operations, and methods

array creation

SHAPE - Size, Helpful, Array, Properties, Elements

operations

DOT - Data, Operations, Transformations

methods

MEAN - Methods, Elements, Arrays, Numbers

Sample Questions

What does the .shape attribute tell you about a NumPy array?

Mnemonic: SHAPE - Size, Helpful, Array, Properties, Elements

Which operator gives the dot product of two matrices in NumPy?

Mnemonic: DOT - Data, Operations, Transformations

What does np.random.randn() generate?

Mnemonic: MEAN - Methods, Elements, Arrays, Numbers
📊

Pandas Operations

Master pandas data manipulation and analysis

data manipulation

CRUD - Create, Read, Update, Delete

analysis

GROUP - Get, Read, Organize, Process, Summarize

Sample Questions

What is the primary data structure in pandas?

Mnemonic: CRUD - Create, Read, Update, Delete

How do you read a CSV file in pandas?

Mnemonic: CRUD - Create, Read, Update, Delete

What does .groupby() do?

Mnemonic: GROUP - Get, Read, Organize, Process, Summarize
💬

Prompt Engineering

Master the art of designing effective prompts for language models

principles

CLEAR - Context, Language, Examples, Accuracy, Refinement

techniques

SMART - Specific, Measurable, Achievable, Relevant, Time-bound

Sample Questions

What is the role of context in prompt engineering?

Mnemonic: CLEAR - Context, Language, Examples, Accuracy, Refinement

What is 'temperature' in LLM settings?

Mnemonic: SMART - Specific, Measurable, Achievable, Relevant, Time-bound

What is 'prompt chaining'?

Mnemonic: CLEAR - Context, Language, Examples, Accuracy, Refinement
🤖

ML Fundamentals

Core concepts in machine learning and inference

workflow

TRAIN - Test, Run, Analyze, Iterate, Normalize

concepts

MODEL - Machine, Output, Data, Evaluation, Learning

Sample Questions

What is the difference between training and inference?

Mnemonic: TRAIN - Test, Run, Analyze, Iterate, Normalize

What is overfitting?

Mnemonic: MODEL - Machine, Output, Data, Evaluation, Learning

What is cross-validation used for?

Mnemonic: TRAIN - Test, Run, Analyze, Iterate, Normalize
🌐

Spatial Data & Vectors

Understanding data in vector spaces and spatial relationships

concepts

SPACE - Spatial, Position, Analysis, Coordinates, Embeddings

operations

DISTANCE - Data, Information, Similarity, Transformation, Analysis, Coordinates, Embeddings

Sample Questions

What is a vector space in the context of data?

Mnemonic: SPACE - Spatial, Position, Analysis, Coordinates, Embeddings

What is the purpose of embeddings in machine learning?

Mnemonic: SPACE - Spatial, Position, Analysis, Coordinates, Embeddings

How is similarity typically measured between vectors?

Mnemonic: DISTANCE - Data, Information, Similarity, Transformation, Analysis, Coordinates, Embeddings

💡 How to Use Mnemonics Effectively

1️⃣

Create Associations

Link mnemonics to real examples and use cases you encounter.

2️⃣

Practice Regularly

Review mnemonics daily and apply them while solving problems.

3️⃣

Visualize Concepts

Create mental images that connect the mnemonic to the concept.

4️⃣

Use in Context

Apply mnemonics while taking quizzes and solving real problems.

5️⃣

Create Your Own

Develop personal mnemonics that work best for your learning style.

6️⃣

Group Study

Share and discuss mnemonics with peers to reinforce learning.

🎓 Ready to test your knowledge? Take a quiz to practice these mnemonics!